30 Statistical Concepts Explained in Simple English

Amused

Village Elder
#1
30 Statistical Concepts Explained in Simple English - Part 15

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on DSC.
Relative Error: Definition, Formula, Examples
Relative Frequency Distribution: Definition and Examples
Relative Frequency Histogram: Definition and How to Make One
Relative Standard Deviation: Definition & Formula
Reliability and Validity in Research: Definitions, Examples
Reporting Statistics APA Style
Research Methods: Qualitative Research and Quantitative Research
Residual Values (Residuals) in Regression Analysis
Residual Plot: Definition and Examples
Sum of Squares: Residual Sum, Total Sum, Explained Sum, Within
Resistance & Resistant Measures in Statistics
Responding Variable
Response Bias: Definition and Examples
Reverse Causality: Definition, Examples
Ridge Regression: Simple Definition
RMSE: Root Mean Square Error
Same Birthday Odds: Higher Than You Think!
Sample in Statistics: What it is, How to find it
Sample Mean: Symbol (X Bar), Definition, and Standard Error
Sample Space Examples and The Counting Principle
Sample Variance: Simple Definition, How to Find it in Easy Steps
Sample Variance: Simple Definition, How to Find it in Easy Steps
Sampling Distribution: Definition, Types, Examples
Sampling Distribution of the Sample Proportion
Sampling Frame / Sample Frame Definition
Sampling Variability: Definition
Sampling With Replacement / Sampling Without Replacement
Scales of Measurement / Level of Measurement
Scale Variable: Definition
Scheffe Test: Definition, Examples, Calculating (Step by Step)
Previous editions, in alphabetical order, can be accessed here: Part 1 | Part 2 | Part 3 | Part 4 | Part 5 | Part 6 | Part 7 | Part 8 | Part 9 | Part 10 | Part 11 | Part 12 | Part 13 | Part 14.
 
#2
Okay I will sign up. But stack overflow and GitHub got me covered. I mostly use R, so the R blog has got enough info. Then there is analytics Vidhya and I practice on Kaggle.
 
#3
Btw if you search Google on statistical concepts, "statistics how-to" will always be among the first websites to show up. But it offers mostly basic info. So you'll have a hard time getting to real solutions. For an advanced statistician you're better off heading to stack overflow or GitHub You can post your entire code and get solutions
 

Amused

Village Elder
#4
Btw if you search Google on statistical concepts, "statistics how-to" will always be among the first websites to show up. But it offers mostly basic info. So you'll have a hard time getting to real solutions. For an advanced statistician you're better off heading to stack overflow or GitHub You can post your entire code and get solutions
I don't search Google. I'm a management scientist. I build some of this stuff.
 

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